Accounting for Bridges use

Accounting for Bridges use varies with the type of node used, which is determined by the type of allocation you have: "Bridges regular", for Bridges' RSM (128GB) nodes); "Bridges GPU", for Bridges' K80 and P100 GPU nodes; Bridges GPU-AI for Bridges' Volta GPU nodes and DGX-2 system; or "Bridges large", for Bridges LSM and ESM (3TB and 12TB) nodes.

Usage is defined in terms of "Service Units" or SUs. The definition of an SU varies with the type of node being used.

Bridges regular

The RSM nodes are allocated as "Bridges regular". This does not include Bridges' GPU nodes. Each RM node holds 28 cores, each of which can be allocated separately. Service Units (SUs) are defined in terms of "core-hours": the use of one core for 1 hour.

1 core-hour = 1 SU

Because the RM nodes each hold 28 cores, if you use one entire RM node for one hour, 28 SUs will be deducted from your allocation.

28 cores x 1 hour = 28 core-hours = 28 SUs

If you use 2 cores on a node for 30 minutes, 1 SU will be deducted from your allocation.

2 cores x 0.5 hours = 1 core-hour = 1 SU

Bridges large

The LSM and ESM nodes are allocated as "Bridges large". Accounting for the LM and ESM nodes is done by the memory requested for the job. Service Units (SUs) are defined in terms of "TB-hours": the use of 1TB of memory for one hour. Note that because the memory requested for a job is set aside for your use when the job begins, SU usage is calculated based on memory requested, not on how much memory is actually used.

1 SU = 1 TB-hour

If your job requests 3TB of memory and runs for 1 hour, 3 SUs will be deducted from your allocation.

3TB x 1 hour = 3TB-hours = 3 SUs

If your job requests 8TB and runs for 6 hours, 48 SUs will be deducted from your allocation.

8TB x 6 hours = 48 TB-hours = 48 SUs

Bridges GPU

Bridges contains two kinds of GPU nodes: NVIDIA Tesla K80s and NVIDIA Tesla P100s. Service Units (SUs) for GPU nodes are defined in terms of "gpu-hours": the use of one GPU Unit for one hour.

Because of the difference in the performance of the nodes, SUs are calculated differently for the two types of nodes.

K80 nodes

The K80 nodes hold 4 GPU units each, each of which can be allocated separately. Service Units (SUs) are defined in terms of gpu-hours:

For K80 GPU nodes, 1 GPU-hour = 1 SU

If you use 2 entire K80 nodes for 1 hour, 8 SUs will be deducted from your allocation.

4 GPU units/node x 2 nodes x 1 hour = 8 gpu-hours = 8 SUs

If you use 2 GPU units for 3 hours, 6 SUs will be deducted from your allocation.

2 GPU units x 3 hours = 6 gpu-hours = 6 SUs

P100 nodes

The P100 nodes hold 2 GPU units each, which can be allocated separately. Service Units (SUs) are defined in terms of GPU-hours. Because the P100s are more powerful than the K80 nodes, the SU definition is different.

For P100 GPU nodes, 1 GPU-hour = 2.5 SUs

If you use an entire P100 node for one hour, 5 SUs will be deducted from your allocation.

2 GPU units/node x 1 node x 1 hour = 2 gpu-hours

2 gpu-hours x 2.5 SUs/gpu-hour = 5 SUs

If you use 1 GPU unit on a P100 for 8 hours, 20 SUs will be deducted from your allocation.

Service Units (SUs) for GPU-AI nodes are defined in terms of "gpu-hours": the use of one GPU Unit for one hour.

DGX-2 node

The DGX-2 node holds 16 GPU units, each of which can be allocated separately. Service Units (SUs) are defined in terms of gpu-hours:

For the DGX-2 node, 1 GPU-hour = 1 SU

If you use 2 GPUs on the DGX-2 node for 1 hour, 2 SUs will be deducted from your allocation.

2 GPU units x 1 hour = 2 gpu-hours = 2 SUs

If you use the entire DGX-2 for 3 hours, 48 SUs will be deducted from your allocation.

16 GPU units x 3 hours = 48 gpu-hours = 48 SUs

Volta 16 nodes

The Volta 16 nodes hold 8 GPU units each, each of which can be allocated separately. Service Units (SUs) are defined in terms of GPU-hours.

For Volta 16 GPU nodes, 1 GPU-hour = 1 SU

If you use an entire Volta 16 node for one hour, 8 SUs will be deducted from your allocation.

8 GPU units/node x 1 node x 1 hour = 8 gpu-hours = 8 SUs

If you use 4 GPU units on a Volta 16 for 48 hours, 196 SUs will be deducted from your allocation.

4 GPU units x 48 hours = 196 gpu-hours = 196 SUs

Accounting for file space

Every Bridges grant has a pylon storage allocation associated with it. If you exceed your storage quota, you will not be able to submit jobs to Bridges.

Each grant has a Unix group associated with it. Every file is "owned" by a Unix group, and that file ownership determines which grant is charged for the file space. See "Managing multiple grants" for a further explanation of Unix groups, and how to manage file ownership if you have more than one grant.

Managing multiple grants

If you have multiple grants on Bridges, you should ensure that the work you do under each grant is assigned correctly to that grant. The files created under or associated with that grant should belong to it, to make them easily available to others on the same grant.

There are two fields associated with each grant for these purposes: a SLURM account id and a Unix group.

Unix groups determine which pylon5 allocation the storage space for files is deducted from, and who owns and can access a file or directory.

For a given grant, the SLURM account id and the Unix group are identical strings.

One of your grants has been designated as your default grant, and the account id and Unix group associated with the grant are your default account id and default Unix group.

When a Bridges job runs, any SUs it uses are deducted from the default grant. Any files created by that job are owned by the default Unix group.

Find your default account id and Unix group

To find your SLURM account ids, use the projects command. It will display all the grants you belong to. It will also list your default account id (called charge id in the projects output) at the top. Your default Unix group is the same.

In this example, the user has two grants with account ids account-1 and account-2. The default account id is account-2.

Use a secondary (non-default) grant

To use a grant other than your default grant on Bridges, you must specify the appropriate account id with the -A option to the SLURM sbatch command. See the Running Jobs section of this Guide for more information on batch jobs, interactive sessions and SLURM.

Note that using the -A option does not change your default Unix group. Any files created during a job are owned by your default Unix group, no matter which account id is used for the job, and the space they use will be deducted from the pylon allocation for the default Unix group.

Change your Unix group for a login session

To temporarily change your Unix group, use the newgrp command. Any files created subsequently during this login session will be owned by the new group you have specified. Their storage will be deducted from the pylon allocation of the new group. After logging out of the session, your default Unix group will be in effect again.

newgrp unix-group

Note that the newgrp command has no effect on the account id in effect. Any Bridges usage will be deducted from the default account id or the one specified with the -A option to sbatch.

Change your default account id and Unix group permanently

You can permanently change your default account id and your default Unix group with the change_primary_group command. Type:

change_primary_group -l

to see all your groups. Then type

change_primary_group account-id

to set account-id as your default.

Your default account id changes immediately. Bridges use by any batch jobs or interactive sessions following this command are deducted from the new account by default.

Your default Unix group does not change immediately. It takes about an hour for the change to take effect. You must log out and log back in after that window for the new Unix group to be the default.

Tracking your usage

There are several ways to track your Bridges usage: the xdusage command, the projects command, and the Grant Management System.

The projects command shows information on all Bridges grants, including usage and the pylon directories associated with the grant.

For more detailed accounting data you can use the Grant Management System. You can also track your usage through the XSEDE User Portal. The xdusage and projects commands and the XSEDE Portal accurately reflect the impact of a Grant Renewal but the Grant Management System currently does not.

Managing your XSEDE allocation

Most account management functions for your XSEDE grant are handled through the XSEDE User Portal. You can search the Knowledge Base to get help. Some common questions:

Changing your default shell

The change_shell command allows you to change your default shell. This command is only available on the login nodes.

To see which shells are available, type

change_shell -l

To change your default shell, type

change_shell newshell

where newshell is one of the choices output by the change_shell -l command. You must use the entire path output by change_shell -l, e.g. /usr/psc/shells/bash. You must log out and back in again for the new shell to take effect.

Acknowledgement in Publications

All publications, copyrighted or not, resulting from an allocation of computing time on Bridges should include an acknowledgement. Please acknowledge both the funding source that supported your access to PSC and the specific PSC resources that you used.

Please also acknowledge support provided by XSEDE's ECSS program and/or PSC staff when appropriate.

Proper acknowledgment is critical for our ability to solicit continued funding to support these projects and next generation hardware.

XSEDE supported research on Bridges

We ask that you use the following text:

This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1548562. Specifically, it used the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center (PSC).

Additional support

ECSS Support

To acknowledge support provided through XSEDE's Extended Collaborative Support Services (ECSS), please use this text:

We thank [consultant name(s)] for [his/her/their] assistance with [describe tasks such as porting, optimization, visualization, etc.], which was made possible through the XSEDE Extended Collaborative Support Service (ECSS) program.

Content of this document

Create or change your PSC password

If you do not already have an active PSC account, you must create a PSC password (also called a PSC Kerberos password) before you can connect to Bridges. Your PSC password is the same on all PSC systems, so if you have an active account on another PSC system, you do not need to reset it before connecting to Bridges.

Your PSC password is separate from your XSEDE Portal password. Resetting one password does not change the other password.

Use the kpasswd command when logged into a PSC system. Do not use the passwd command.

When you change your PSC password, whether you do it via the online utility or via the kpasswd command on one PSC system, you change it on all PSC systems.

Connect to Bridges

When you connect to Bridges, you are connecting to one of Bridges' login nodes. The login nodes are used for managing files, submitting batch jobs and launching interactive sessions. They are not suited for production computing.

See the Running Jobs section of this User Guide for information on production computing on Bridges.

There are several methods you can use to connect to Bridges.

You can access Bridges through a web browser by using the OnDemand software. You will still need to understand Bridges' partition structure and the options which specify job limits like time and memory use, but OnDemand provides a more modern, graphical interface to Bridges.

This document explains how to use ssh, gsissh or XSEDE Single Sign On to access Bridges.

SSH

You can use an ssh client from your local machine to connect to Bridges using either your PSC or XSEDE credentials.

SSH is a program that enables secure logins over an unsecure network. It encrypts the data passing both ways so that if it is intercepted it cannot be read.

SSH is client-server software, which means that both the user's local computer and the remote computer must have it installed. SSH server software is installed on all the PSC machines. You must install SSH client software on your local machine.

Once you have an ssh client installed, you can use either your PSC credentials or XSEDE credentials (optionally with DUO MFA) to connect to Bridges. Note that you must have created your PSC password before you can use ssh to connect to Bridges.

Use ssh to connect to Bridges using XSEDE credentials and (optionally) DUO MFA:

Using your ssh client, connect to hostname bridges.psc.xsede.org or bridges.psc.edu using port 2222. Either hostname will connect you to Bridges, but you must specify port 2222.

Enter your XSEDE username and password when prompted.

(Optional) If you are registered with XSEDE DUO, you will receive a prompt on your phone. Once you have answered it, you will be logged in.

Use ssh to connect to Bridges using PSC credentials:

Using your ssh client, connect to hostname bridges.psc.xsede.org or bridges.psc.edu using the default port (22). Either hostname will connect you to Bridges. You do not have to specify the port.

gsissh

If you have installed the Globus toolkit you can use gsissh to connect to Bridges. Gsissh is a version of ssh which uses certificate authentication. Use the command myproxy-logon to get a suitable certificate. The Globus toolkit includes a man page for myproxy-logon.

Containers

Containers are stand-alone packages holding the the software needed to create a very specific computing environment. If you need a very specialized environment, you can create your own container or use one that is already installed on Bridges. Singularity is the only type of container supported on Bridges.

However, in many cases, Bridges has all the software you will need. Before creating a container for your work, check the extensive list of software that has been installed on Bridges. While logged in to Bridges, you can also get a list of installed packages by typing

module avail

If you need a package that is not available on Bridges, you can request that it be installed by emailing bridges@psc.edu. You can also install software packages in your own file spaces and, in some cases, we can provide assistance if you encounter difficulties.

Containers available on Bridges

We have installed many containers from the NVIDIA GPU Cloud (NGC) on Bridges. These containers are fully optimized, GPU-accelerated environments for AI, machine learning and HPC. They can be used on the Bridges-AI (Volta 16 and DGX-2) nodes and on some RM-GPU nodes (P100 GPU only).

Creating a container

Singularity is the only container software supported on Bridges. You can create a Singularity container, copy it to Bridges and then execute your container on Bridges, where it can use Bridges' compute nodes and filesystems. In your container you can use any software required by your application: a different version of CentOS, a different Unix operating system, any software in any specific version needed. You can install your Singularity container without any intervention from PSC staff.

Data Collections

A community dataset space allows Bridges' users from different grants to share data in a common space. Bridges hosts both public and private datasets, providing rapid access for individuals, collaborations and communities with appropriate protections.

Community datasets are appropriate when data will be shared amongst Bridges' groups. Any data that should only be accessed by one group should be stored in that group's pylon5 space.

If you have a dataset for use by multiple groups on Bridges, request that it be stored in the community dataset space by completing the Community Dataset Request form. If your data collection has security or compliance requirements, you should indicate so on the form, or you can contact compliance@psc.edu.

Natural Languge Tool Kit Data

NLTK comes with many corpora, toy grammars, trained models, etc. A complete list of the available data is posted at: http://nltk.org/nltk_data/

Available on Bridges at /pylon5/datasets/community/nltk

MNIST

Dataset of handwritten digits used to train image processing systems.

Available on Bridges at /pylon5/datasets/community/mnist

Genomics Data

Several genomics datasets are publicly available.

BLAST

The BLAST databases can be accessed through the environment variable $BLASTDB after loading the BLAST module.

CAMI

CAMI (Critical Assessment of Metagenome Interpretation) is a community-led initiative designed to help tackle challenges in metagenome assembly and analysis by aiming for an independent, comprehensive and bias-free evaluation of methods. Data from the first CAMI challenge is available at /pylon5/datasets/community/genomics/cami.

RepBase

Repbase is the most commonly used database of repetitive DNA elements. You must register with RepBase at http://www.girinst.org and send proof of registration to genomics@psc.edu in order to use the Repbase database.

UCSC

The University of California at Santa Cruz reference genomes are available at /pylon5/datasets/community/genomics/UCSC. The collection includes human, mouse and drosophila genomes.

Other genomics datasets

Other available datasets are typically used with a particular genomics package. These include:

File expiration

Three months after your grant expires all of your Bridges files associated with that grant will be deleted, no matter which file space they are in. You will be able to login during this 3-month period to transfer files, but you will not be able to run jobs or create new files.

File permissions

Access to files in any Bridges space is governed by Unix file permissions. If your data has additional security or compliance requirements, please contact compliance@psc.edu.

Unix file permissions

For detailed information on Unix file protections, see the man page for the chmod command.

To share files with your group, give the group read and execute access for each directory from your top-level directory down to the directory that contains the files you want to share.

chmod g+rx directory-name

Then give the group read and execute access to each file you want to share.

chmod g+rx filename

To give the group the ability to edit or change a file, add write access to the group:

chmod g+rwx filename

Access Control Lists

If you want more fine-grained control than Unix file permissions allow ---for example, if you want to give only certain members of a group access to a file, but not all members---then you need to use Access Control Lists (ACLs). Suppose, for example, that you want to give janeuser access to a file in a directory, but no one else in the group.

Use the setfacl (set file acl) command

setfacl -m user:janeuser:rx directory-name

for each directory from your top-level directory down to the directory that contains the file you want to share with janeuser. Then give janeuser access to a specific file with

setfacl -m user:janeuser:r filename

User janeuser will now be able to read this file, but no one else in the group will have access to it.

To see what ACLs are set on a file, use the getfacl command.

There are man pages for chmod, setfacl and getfacl.

Home ($HOME)

This is your Bridges home directory. It is the usual location for your batch scripts, source code and parameter files. Its path is /home/username, where username is your PSC userid. You can refer to your home directory with the environment variable $HOME. Your home directory is visible to all of Bridges's nodes.

Your home directory is backed up daily, although it is still a good idea to store copies of your important files in another location, such as the pylon5 file system or on a local file system at your site. If you need to recover a home directory file from backup send email to remarks@psc.edu. The process of recovery will take 3 to 4 days.

$HOME quota

Your home directory has a 10GB quota. You can check your home directory usage using the quota command or the command du -sh. To improve the access speed to your home directory files you should stay as far below your home directory quota as you can.

Grant expiration

Three months after a grant expires, the files in your home directory associated with that grant will be deleted.

pylon5 ($SCRATCH)

Pylon5 is a Lustre file system shared across all of Bridges' nodes. It is available on Bridges compute nodes as $SCRATCH.

The pylon5 file system is persistent storage, and can be used as working space for your running jobs. It provides fast access for data read or written by running jobs. IO to pylon5 is much faster than to your home directory.

Files on pylon5 are not backed up, so you should store copies of important pylon5 files in another location.

pylon5 directories

The path of your pylon5 home directory is /pylon5/groupname/username, where groupname is the Unix group associated with your grant. Use the id command to find your group name.

The command id -Gn will list all the Unix groups you belong to.The command id -gn will list the Unix group associated with your current session.

If you have more than one grant, you will have a pylon5 directory for each grant. Be sure to use the appropriate directory when working with multiple grants.

pylon5 quota

Your usage quota for each of your grants is the Pylon storage allocation you received when your proposal was approved. If your total use in pylon5 exceeds this quota your access to the partitions on Bridges will be shut off until you are under quota.

Use the du -sh or projects command to check your pylon5 usage. You can also check your usage on the XSEDE User Portal.

If you have multiple grants, it is very important that you store your files in the correct pylon5 directory.

Grant expiration

Three months after a grant expires, the files in any pylon5 directories associated with that grant will be deleted.

Node-local ($LOCAL)

Each of Bridges's nodes has a local file system attached to it. This local file system is only visible to the node to which it is attached. The local file system provides fast access to local storage.

This file space is available on all nodes as $LOCAL.

If your application performs a lot of small reads and writes, then you could benefit from using $LOCAL.

$LOCAL is only available when your job is running, and can only be used as working space for a running job. Once your job finishes your local files are inaccessible and deleted. To use local space, copy files to $LOCAL at the beginning of your job and back out to a persistent file space before your job ends.

If a node crashes all the $LOCAL files are lost. Therefore, you should checkpoint your $LOCAL files by copying them to pylon5 during long runs.

Multi-node jobs

If you are running a multi-node job the variable $LOCAL points to the local file space on the node that is running your rank 0 process.

$LOCAL size

The maximum amount of local space varies by node type. The RSM (128GB) and GPU nodes have a maximum of 3.7TB. The LSM (3TB) nodes have a maximum of 14TB and the ESM (12TB) nodes have a maximum of 49TB.

To check on your local file space usage type:

du -sh

No Service Units accrue for the use of $LOCAL.

Using $LOCAL

To use $LOCAL you must first copy your files to $LOCAL at the beginning of your script, before your executable runs. The following script is an example of how to do this

Set $sourcedir to point to the directory that contains the files to be copied before you execute your program. This code will try at most 20 times to copy your files. If it succeeds, the loop will exit. If an invocation of rsync was unsuccessful, the loop will try again and pick up where it left off.

At the end of your job you must copy your results back from $LOCAL or they will be lost. The following script will do this.

This code fragment copies your files to a directory in your pylon5 file space named results, which you must have created previously with the mkdir command. Again it will loop at most 20 times and stop if it is successful.

Memory files ($RAMDISK)

You can also use the memory allocated for your job for IO rather than using disk space. This will offer the fastest IO on Bridges.

In a running job the environment variable $RAMDISK will refer to the memory associated with the nodes in use.

The amount of memory space available to you depends on the size of the memory on the nodes and the number of nodes you are using. You can only perform IO to the memory of nodes assigned to your job.

If you do not use all of the cores on a node, you are allocated memory in proportion to the number of cores you are using. Note that you cannot use 100% of a node's memory for IO; some is needed for program and data usage.

$RAMDISK is only available to you while your job is running, and can only be used as working space for a running job. Once your job ends this space is inaccessible. To use memory files, copy files to $RAMDISK at the beginning of your job and back out to a permanent space before your job ends. If your job terminates abnormally your memory files are lost.

Within your job you can cd to $RAMDISK, copy files to and from it, and use it to open files. Use the command du -sh to see how much space you are using.

If you are running a multi-node job the $RAMDISK variable points to the memory space on the node that is running your rank 0 process.

Gateways

Bridges hosts a number of gateways - web-based, domain-specific user interfaces to applications, functionality and resources that allow users to focus on their research rather than programming and submitting jobs. Gateways provide intuitive, easy-to-use interfaces to complex functionality and data-intensive workflows.

Gateways can manage large numbers of jobs and provide collaborative features, security constraints and provenance tracking, so that you can concentrate on your analyses instead of on the mechanics of accomplishing them.

Researchers preparing de novo transcriptome assemblies via the popular Galaxy platform for data-intensive analysis have transparent access to Bridges, without the need to obtain their own XSEDE allocation. Bridges is ideal for rapid assembly of massive RNA sequence data.

A high-performance Trinity tool has been installed on the public Galaxy Main instance at usegalaxy.org. All Trinity jobs in workflows run from usegalaxy.org will execute transparently on large memory nodes on Bridges. These tools are free to use for open scientific research.

The Causal Web Portal, from the Center for Causal Discovery, offers easy to use software for causal discovery from large and complex biomedical datasets, applying Bayesan and constraint based algorithms. It includes a web application as well as API’s and a command line version.

Using Bridges' GPU nodes

The NVIDIA Tesla K80 and P100 GPUs on Bridges provide substantial, complementary computational power for deep learning, simulations and other applications.

A standard NVIDIA accelerator environment is installed on Bridges' GPU nodes. If you have programmed using GPUs before, you should find this familiar. Please contact bridges@psc.edu for more help.

GPU Nodes

There are two types of GPU nodes on Bridges: 16 nodes with NVIDIA K80 GPUs and 32 nodes with NVIDIA P100 GPUs.

K80 nodes: The 16 K80 GPU nodes each contain 2 NVIDIA K80 GPU cards, and each card contains two GPUs that can be individually scheduled. Ideally, the GPUs are shared in a single application to ensure that the expected amount of on-board memory is available and that the GPUs are used to their maximum capacity. This makes the K80 GPU nodes optimal for applications that scale effectively to 2, 4 or more GPUs. Some examples are GROMACS, NAMD and VASP. Applications using a multiple of 4 K80 GPUs will maximize system throughput.

P100 nodes: The 32 P100 GPU nodes contain 2 NVIDIA P100 GPU cards, and each card holds one very powerful GPU, optimally suited for single-GPU applications that require maximum acceleration. The most prominent example of this is deep learning training using frameworks that do not use multiple GPUs.

File Systems

The /home and pylon5 file systems are available on all of these nodes. See the File Spaces section of the User Guide for more information on these file systems.

Compiling and Running jobs

Use the GPU partition, either in batch or interactively, to compile your code and run your jobs. See the Running Jobs section of the User Guide for more information on Bridges' partitions and how to run jobs.

CUDA

To use CUDA, first you must load the CUDA module. To see all versions of CUDA that are available, type:

module avail cuda

Then choose the version that you need and load the module for it.

module load cuda

loads the default CUDA. To load a different version, use the full module name.

module load cuda/8.0

CUDA 8 codes should run on both types of Bridges' GPU nodes with no issues. CUDA 7 should only be used on the K80 GPUs (Phase 1). Performance may suffer with CUDA 7 on the P100 nodes (Phase 2).

OpenACC

Our primary GPU programming environment is OpenACC.

The PGI compilers are available on all GPU nodes. To set up the appropriate environment for the PGI compilers, use the module command:

If you will be using these compilers often, it will be useful to add this command to your shell initialization script.

There are many options available with these compilers. See the online man pages (“man pgf90”,”man pgcc”,”man pgCC”) for detailed information. You may find these basic OpenACC options a good place to start:

pgcc –acc yourcode.c
pgf90 –acc yourcode.f90

P100 node users should add the “-ta=tesla,cuda8.0” option to the compile command, for example:

Hadoop and Spark

If you want to run Hadoop or Spark on Bridges, you should note that when you apply for your account.

File systems

/home

The /home file system, which contains your home directory, is available on all Bridges' Hadoop nodes.

HDFS

The Hadoop filesystem, HDFS, is available from all Hadoop nodes. There is no explicit quota for the HDFS, but it uses your $SCRATCH disk space. Please delete any files you don't need when your job has ended.

Files must reside in HDFS to be used in Hadoop jobs. Putting files into HDFS requires these steps:

Transfer the files to the namenode with scp or sftp

Format them for ingestion into HDFS

Use the hadoop fs -put command to copy the files into HDFS. This command distributes your data files across the cluster's datanodes.

The hadoop fs command should be in your command path by default.

Documentation for the hadoop fs command lists other options. These options can be used to list your files in HDFS, delete HDFS files, copy files out of HDFS and other file operations.

To request the installation of data ingestion tools on the Hadoop cluster send email to bridges@psc.edu.

Accessing the Hadoop /Spark cluster

To start using Hadoop and Spark with Yarn and HDFS on Bridges, connect to the login node and issue the following commands:

interact -N 3 # you will need to wait until resources are allocated to you before continuing
module load hadoop
start-hadoop.sh

Your cluster will be set up and you'll be able to run hadoop and spark jobs. The cluster requires a minimum of three nodes (-N 3). Larger jobs may require a reservation. Please contact bridges@psc.edu if you would like to use more than 8 nodes or run for longer than 8 hours.

Please note that when your job ends your HDFS will be unavailable so be sure to retrieve any data you need before your job finishes.

Web interfaces are currently not available for interactive jobs but can be made available for reservations.

Spark

The Spark data framework is available on Bridges. Spark, built on the HDFS filesystem, extends the Hadoop MapReduce paradigm in several directions. It supports a wider variety of workflows than MapReduce. Most importantly, it allows you to process some or all of your data in memory if you choose. This enables very fast parallel processing of your data.

Python, Java and Scala are available for Spark applications. The pyspark interpreter is especially effective for interactive, exploratory tasks in Spark. To use Spark you must first load your data into Spark's highly efficient file structure called Resilient Distributed Dataset (RDD).

Extensive online documentation is available at the Spark web site. If you have questions about or encounter problems using Spark, send email to bridges@psc.edu.

Spark example using Yarn

Here is an example command to run a Spark job using yarn. This example calculates pi using 10 iterations.

where yarnapplicationId is the yarn applicationId assigned by the cluster.

A simple Hadoop example

This section demonstrates how to run a MapReduce Java program on the Hadoop cluster. This is the standard paradigm for Hadoop jobs. If you want to run jobs using another framework or in other languages besides Java send email to bridges@psc.edu for assistance.

Follow these steps to run a job on the Hadoop cluster. All the commands listed below should be in your command path by default. The variable HADOOP_HOME should be set for you also.

Compile your Java MapReduce program with a command similar to:

hadoop com.sun.tools.javac.Main WordCountWordCount.java

where:

WordCount is the name of the output directory where you want your class file to be put

WordCount.java is the name of your source file

Create a jar file out of your class file with a command similar to:

jar -cvf WordCount.jar -C WordCount/ .

where:

WordCount.jar is the name of your output jar file

WordCount is the name of the directory which contains your class file

Make an input directory in the HDFS, if it doesn't already exist:

hdfs dfs -mkdir -p /datasets

Transfer an input file to the /datasets directory in HDFS

hdfs dfs -put /home/training/hadoop/datasets/compleat.txt /datasets

Launch your Hadoop job with the hadoop command

Once you have your jar file you can run the hadoop command to launch your Hadoop job. Your hadoop command will be similar to

/datasets/compleat.txt is the path to your input file in the HDFS file system. This file must already exist in HDFS.

$MYOUTPUT is the path to your output file, which will be saved in the HDFS file system. You must set this variable to the output file path before you issue the hadoop command.

After you issue the hadoop command your job is controlled by the Hadoop scheduler to run on the datanodes. The scheduler is currently a stricty FIFO scheduler. If your job turnaround is not meeting your needs send email to bridges@psc.edu.

When your job finishes, the hadoop command will end and you will be returned to the system prompt.

Other Hadoop technologies

An entire ecosystem of technologies has grown up around Hadoop, such as HBase and Hive. To request the installation of a different package send email to bridges@psc.edu.

OnDemand

The OnDemand interface allows you to conduct your research on Bridges through a web browser. You can manage files - create, edit and move them - submit and track jobs, see job output, check the status of the queues, run a Jupyter notebook through JupyterHub and more, without logging in to Bridges via traditional interfaces.

OnDemand was created by the Ohio Supercomputer Center (OSC). This document provides an outline of how to use OnDemand on Bridges. For more help, check the extensive documentation for OnDemand created by OSC, including many video tutorials, or email bridges@psc.edu.

Start OnDemand

You will be prompted for a username and password. Enter your PSC username and password.

The OnDemand Dashboard will open. From this page, you can use the menus across the top of the page to manage files and submit jobs to Bridges.

To end your OnDemand session, choose Log Out at the top right of the Dashboard window and close your browser.

Manage files

To create, edit or move files, click on the Files menu from the Dashboard window. A dropdown menu will appear, listing all your file spaces on Bridges: your home directory and the pylon directories for each of your Bridges' grants.

Choosing one of the file spaces opens the File Explorer in a new browser tab. The files in the selected directory are listed. No matter which directory you are in, your home directory is displayed in a panel on the left.

There are two sets of buttons in the File Explorer.

Buttons on the top left just below the name of the current directory allow you to View, Edit, Rename, Download, Copy or Paste (after you have moved to a different directory) a file, or you can toggle the file selection with (Un)Select All.

Buttons on the top of the window on the right perform these functions:

Go To

Navigate to another directory or file system

Open in Terminal

Open a terminal window on Bridges in a new browser tab

New File

Creates a new empty file

New Dir

Create a new subdirectory

Upload

Copies a file from your local machine to Bridges

Show Dotfiles

Toggles the display of dotfiles

Show Owner/Mode

Toggles the display of owner and permisson settings

Create and edit jobs

You can create new job scripts and edit existing scripts, and submit those scripts to Bridges through OnDemand.

From the top menus in the Dashboard window, choose Jobs > Job Composer. A Job Composer window will open.

There are two tabs at the top: Jobs and Templates.

In the Jobs tab, a listing of your jobs is gven.

Create a new job script

To create a new job script:

Select a template to begin with

Edit the job script

Edit the job options

Select a template

Go to the Jobs tab in the Jobs Composer window. You have been given a default template, named Simple Sequential Job.

To create a new job script, click the blue New Job > From Default Template button in the upper left. You will see a green message at the top of the window, "Job was successfully created".

At the right of the Jobs window, you will see the Job Details, including the location of the script and the script name (by default, main_job.sh). Under that, you will see the contents of the job script in a section titled Submit Script.

Edit the job script

Edit the job script so that it has the commands and workflow that you need.

If you do not want the default settings for a job, you must include options to change them in the job script. For example, you may need more time or more than one node. For the GPU partitions, you must specify the type and number of GPUs you want. For the LM partition, you must specify how much memory you need. Use an SBATCH directive in the job script to set these options.

Click the blue Edit Files button at the top of the Jobs tab in the Jobs Composer window

In the Jobs tab in the Jobs Composer window, find the Submit Script section at the bottom right. Click the blue Open Editor button.

After you save the file, the editor window remains open, but if you return to the Jobs Composer window, you will see that the content of your script has changed.

Edit the job options

In the Jobs tab in the Jobs Composer window, click the blue Job Options button. The options for the selected job such as name, the job script to run, and the account it run under are displayed and can be edited. Click Save or Cancel to return to the job listing.

Submit jobs to Bridges

Select a job in the Jobs tab in the Jobs Composer window. Click the green Submit button to submit the selected job. A message at the top of the window shows whether the job submission was successful or not. If it is not, you can edit the job script or options and resubmit. When the job submits successfully, the status of the job in the Jobs Composer window will change to Queued or Running. When the job completes, the status will change to Completed.

JupyterHub and IJulia

You can run JupyterHub, and IJulia notebooks, through OnDemand. You must do some setup before the first time you run IJulia through OnDemand.

Setup IJulia for OnDemand use

You only need to do this once.

While logged in to Bridges, request an interactive session with access to sites external to Bridges by typing:

interact --egress

Once the sesstion starts, type these commands:

module load anaconda3
module load julia
julia

When Julia starts, type

Pkg.add("IJulia")

When you see the message that IJulia has been installed, you can end your interactive session.

Select Interactive Apps >> Jupyter Notebooks from the top menu in the Dashboard window.

In the screen that opens, specify the timelimit, number of nodes, and partition to use. If you have more than one grant on Bridges, you can also designate the account to deduct this usage from .

If you will use the LM or one of the GPU partitions, you must add a flag in the Extra Args field for the amount of memory or the number and type of GPUs you want:

--mem=numberGB

--gres=gpu:type:number

See the Running jobs section of this User Guide for more information on Bridges' partitions and the options available.

Click the blue Launch button to start your JupyterHub session. You may have to wait in the queue for resources to be available.

When your session starts, click the blue Connect to Jupyter button. The Dashboard window now displays information about your JupyterHub session including which node it is running on, when it began, and how much time remains.

A new window running JupyterHub also opens. Note the three tabs: Files, Running and Clusters.

Files

By default you are in the Files tab, and it displays the contents of your Bridges home directory. You can navigate through your home directory tree.

Running

Under the Running tab, you will see listed any notebooks or terminal sessions that you are currently running.

Now you can start a Jupyter or IJulia notebook:

To start a Jupyter notebook which is stored in your home directory space, in the Files tab, click on its name. A new window running the notebook opens.

To start a Jupyter notebook which is stored in your pylon5 directory, you must first create a symbolic link to it from your home directory. While in your home directory, use a command like

ln -s /pylon5/yourgroup/youruserid PYLONDIR

When you enter JuypterHub, you will see the entry PYLONDIR in your list of files under the Files tab. Click on this to be moved to your pylon5 directory.

To start IJulia, in the Files tab, click on the New button at the top right of the file listing. Choose IJulia from the drop down.

Errors

If you get an "Internal Server Error" when starting a JupyterHub session, you may be over your home directory quota. Check the Details section of the error for a line like:

This command shows the amount of storage in your home directory. Home directory quotas are 10GB. If du -sh shows you are near 10GB, you should delete or move some files out of your home directory. You can do this in OnDemand in the File Explorer window or in a shell access window.

When you are under quota, you can try starting a JupyterHub session again.

Stopping your JupyterHub session

In the Dashboard window, click the red Delete button.

RStudio

You can run RStudio through OnDemand.

Select Interactive Apps > RStudio Server from the top menu in the Dashboard window.

In the screen that opens, specify the timelimit, number of nodes, and partition to use. You can also designate the account to apply this usage to if you have more than one grant on Bridges.

Use the Extra Args field to ask for specific resources.

If you will use the LM partition, you must add a flag in the Extra Args field for the amount of memory you need:

--mem=numberGB

If you will use one of the GPU partitions, you must add a flag in the Extra Args field for the number and type of GPUs you want:

--gres=gpu:type:number

If you want to add additional external packages to your User Library, you must use the -C EGRESS flag in the Extra Args field to allow access to external sites:

-C EGRESS

See the Running jobs section of this User Guide for more information on Bridges' partitions and the options available.

Click the blue Launch button to start your RStudio session. You may have to wait in the queue for resources to be available.

When your session starts, click the blue Connect to RStudio Server button. A new window opens with the RStudio interface.

Installed Packages

The Packages tab in the lower right pane of the RStudio interface lists all the packages currently installed in your User Library and in the System Library. To install additional packages into your user library location, click the Install link under the Packages tab. A pop-up window will open asking where to find the package (either the CRAN repository or a Package Archive file).

If you choose CRAN repository, you can type the names of the package(s) you want in the Packages field.

If you choose Package Archive File, you can browse for the file you want. By default your home directory space on Bridges will be shown. To browse through your pylon5 space, click on "..." at the right end of the Home row and enter pylon5/yourgroup in the Path to Folder field in the pop-up window.

Errors

If you exceed the timelimit you requested when setting up your RStudio session, you will see this error:

Error: Status code 503 returned

To continue using RStudio, go to Interactive Apps > RStudio from the top menu in the Dashboard window and start a new session.

Stopping your RStudio session

To end your RStudio session, either select File > Quit Session or click the red icon in the upper right of your RStudio window. NOTE that this only closes your RStudio session; it does not close your interactive Bridges session. You are still accruing Service Units. If you like, you can start another RStudio session.

To end your interactive Bridges session so that you are no longer accruing Service Units, return to the Dashboard window and click the red Delete button.

Shell access

You can get shell access to Bridges by choosing Clusters > >_Bridges Shell Access from the top menus in the Dashboard window. In the window that opens, you are logged in to one of Bridges' login nodes as if you used ssh to connect to Bridges.

Miscellaneous

Accessing Bridges documentation

In the Dashboard window, under the Help menu, choose Online Documentation to be taken to the Bridges User Guide.

Change your PSC password

In the Dashboard window, under the Help menu, choose Change HPC Password to be taken to the PSC password change utility.

MPI programming

Three types of MPI are supported on Bridges: MVAPICH2, OpenMPI and Intel MPI.

There are two steps to compile an MPI program:

If you are using MVAPICH2 or OpenMPI, load the correct module for the compiler and MPI type you want to use. The Intel MPI module is loaded for you on login.

Issue the appropriate MPI wrapper command to compile your program

The three MPI types may perform differently on different problems or in different programming environments. If you are having trouble with one type of MPI, please try using another type. Contact bridges@psc.edu for more help.

Other languages

Debugging and performance analysis

DDT

DDT is a debugging tool for C, C++ and Fortran 90 threaded and parallel codes. It is client-server software. Install the client on your local machine and then you can access the GUI on Bridges to debug your code.

VTune

VTune is a performance analysis tool from Intel for serial, multithreaded and MPI applications. Install the client on your local machine and then you can access the GUI on Bridges. See the VTune page for more information.

Running Jobs

All production computing must be done on Bridges' compute nodes, NOT on Bridges' login nodes. The SLURM scheduler (Simple Linux Utility for Resource Management) manages and allocates all of Bridges' compute nodes. Several partitions, or job queues, have been set up in SLURM to allocate resources efficiently.

To run a job on Bridges, you need to decide how you want to run: interactively, in batch, or through OnDemand; and where to run - that is, which partitions you are allowed to use.

What are the different ways to run a job?

You can run jobs in Bridges in several ways:

interactive mode - where you type commands and receive output back to your screen as the commands complete

batch mode - where you first create a batch (or job) script which contains the commands to be run, then submit the job to be run as soon as resources are available

Regardless of which way you choose to run your jobs, you will always need to choose a partition to run them in.

Which partitions can I use?

Different partitions control different types of Bridges' resources; they are configured by the type of node they control along with other job requirements like how many nodes or how much time or memory is needed. Your access to the partitions is based on the type of Bridges allocation that you have ("Bridges regular memory", "Bridges large memory", 'Bridges GPU", or "Bridges-AI"). You may have more than one type of allocation; in that case, you will have access to more than one set of partitions.

Interactive sessions

You can do your production work interactively on Bridges, typing commands on the command line, and getting responses back in real time. But you must be allocated the use of one or more Bridges' compute nodes by SLURM to work interactively on Bridges. You cannot use the Bridges login nodes for your work.

You can run an interactive session in any of the SLURM partitions. You will need to specify which partition you want, so that the proper resources are allocated for your use.

If all of the resources set aside for interactive use are in use, your request will wait until the resources you need are available. Using a shared partition (RM-shared, GPU-shared) will probably allow your job to start sooner.

The interact command

To start an interactive session, use the command interact. The format is

interact -options

The simplest interact command is

interact

This command will start an interactive job using the defaults for interact, which are:

Partition: RM-smallCores: 1Time limit: 60 minutes

Once the interact command returns with a command prompt you can enter your commands. The shell will be your default shell. When you are finished with your job, type CTRL-D.

Note: Files created during a job will be owned by the Unix group in effect when the job is submitted. This may be different than the account id for the job. See the discussion of the newgrp command in the Account Administration section of this User Guide to see how to change the Unix group currently in effect.

Your default account id

-R reservation-name

Reservation name, if you have oneUse of -R does not automatically set any other interact options. You still need to specify the other options (partition, walltime, number of nodes) to override the defaults for the interact command. If your reservation is not assigned to your default account, then you will need to use the -A option when you issue your interact command.

No default

--mem=nGBNote the "--" for this option

Amount of memory requested in GB. This option should only be used for the LM partition.

No default

--gres=gpu:type:nNote the "--" for this option

Specifies the type and number of GPUs requested.

'type' is one of: volta32, volta16, p100 or k80.

For the GPU, GPU-shared and GPU-small partitions, type is either k80 or p100. The default is k80.

Batch jobs

To run a batch job, you must first create a batch (or job) script, and then submit the script using the sbatch command.

A batch script is a file that consists of SBATCH directives, executable commands and comments.

SBATCH directives specify your resource requests and other job options in your batch script. You can also specify resource requests and options on the sbatch command line. Any options on the command line take precedence over those given in the batch script. The SBATCH directives must start with '#SBATCH' as the first text on a line, with no leading spaces.

Comments begin with a '#' character.

The first line of any batch script must indicate the shell to use for your batch job.

The sbatch command

The options to sbatch can either be in your batch script or on the sbatch command line. Options in the command line override those in the batch script.

Note:

Be sure to use the correct account id your job if you have more than one grant. See the -A option for sbatch to change the SLURM account id for a job. Information on how to determine your valid account ids and change your default account id is in the Account adminstration section of the Bridges User Guide.

In some cases, the options for sbatch differ from the options for interact or srun.

By default, sbatch submits jobs to the RM partition. Use the -p option for sbatch to direct your job to a different partition.

Options to the sbatch command

For more information about these options and other useful sbatch options see the sbatch man page.

Note: Files created during a job will be owned by the Unix group in effect when the job is submitted. This may be different than the account id used by the job. See the discussion of the newgrp command in the Account Administration section of this User Guide to see how to change the Unix group currently in effect.

Your default account id

--res reservation-nameNote the "--" for this option

Use the reservation that has been set up for you. Use of --res does not automatically set any other options. You still need to specify the other options (partition, walltime, number of nodes) that you would in any sbatch command. If your reservation is not assigned to your default account then you will need to use the -A option to sbatch to specify the account.

NA

--mem=nGBNote the "--" for this option

Memory in GB. This option is only valid for the LM partition.

None

-C constraints

Specifies constraints which the nodes allocated to this job must satisfy.

An sbatch command can have only one -C option. Multiple constraints can be specified with "&". For example, -C LM&PH2 constrains the nodes to 3TB nodes with 20 cores and 38.5GB/core. If mutilple -C commands are given, (e.g., sbatch ..... -C LM -C EGRESS) only the last applies. The -C LM option will be ignored in this example.

Ensures that a job in the LM partition uses only the 3TB nodes. This option is required for any jobs in the LM partition which use /pylon5.

PH1

Ensures that the job will run on LM nodes which have 16 cores and 48GB/core

PH2

Ensures that the job will run on LM nodes which have 20 cores and 38.5GB/core

PERF

Turns on performance profiling. For use with performance profiling software like VTune, TAU

See the discussion of the -C option in the sbatch man page for more information.

None

--gres=gpu:type:nNote the "--" for this option

Specifies the type and number of GPUs requested.

'type' is one of: volta32, volta16, p100 or k80.

For the GPU, GPU-shared and GPU-small partitions, type is either k80 or p100. The default is k80.

For the GPU-AI partition, type is either volta16 or volta32

'n' is the number of GPUs. Valid choices are

1-4, when type=k80

1-2, when type=p100

1-8 when type=volta16

1-16 when type=volta32

None

--ntasks-per-node=nNote the "--" for this option

Request n cores be allocated per node.

1

--mail-type=typeNote the "--" for this option

Send email when job events occur, where type can be BEGIN, END, FAIL or ALL.

None

--mail-user=userNote the "--" for this option

User to send email to as specified by -mail-type. Default is the user who submits the job.

None

-d=dependency-list

Set up dependencies between jobs, where dependency-list can be:

after:job_id[:jobid...]

This job can begin execution after the specified jobs have begun execution.

afterany:job_id[:jobid...]

This job can begin execution after the specified jobs have terminated.

aftercorr:job_id[:jobid...]

A task of this job array can begin execution after the corresponding task ID in the specified job has completed successfully (ran to completion with an exit code of zero).

afternotok:job_id[:jobid...]

This job can begin execution after the specified jobs have terminated in some failed state (non-zero exit code, node failure, timed out, etc).

afterok:job_id[:jobid...]

This job can begin execution after the specified jobs have successfully executed (ran to completion with an exit code of zero).

singleton

This job can begin execution after any previously launched jobs sharing the same job name and user have terminated.

None

--no-requeueNote the "--" for this option

Specifies that your job will be not be requeued under any circumstances. If your job is running on a node that fails it will not be restarted. Note the "--" for this option.

NA

--time-min=HH:MM:SSNote the "--" for this option.

Specifies a minimum walltime for your job in HH:MM:SS format.

SLURM considers the walltime requested when deciding which job to start next. Free slots on the machine are defined by the number of nodes and how long those nodes are free until they will be needed by another job. By specifying a minimum walltime you allow the scheduler to reduce your walltime request to your specified minimum time when deciding whether to schedule your job. This could allow your job to start sooner.

If you use this option your actual walltime assignment can vary between your minimum time and the time you specified with the -t option. If your job hits its actual walltime limit, it will be killed. When you use this option you should checkpoint your job frequently to save the results obtained to that point.

None

--switches=1--switches=1@HH:MM:SSNote the "--" for this option

Requests that the nodes your job runs on all be on one switch, which is a hardware grouping of 42 nodes.

If you are asking for more than 1 and fewer than 42 nodes, your job will run more efficiently if it runs on one switch. Normally switches are shared across jobs, so using the switches option means your job may wait longer in the queue before it starts.

The optional time parameter gives a maximum time that your job will wait for a switch to be available. If it has waited this maximum time, the request for your job to be run on a switch will be cancelled.

See the -A option to the sbatchor interact commands to set the SLURM account id for a specific job.

The newgrp command will change your Unix group for that login session only. Note that any files created by a job are owned by the Unix group in effect when the job is submitted,which is not necessarily the same as the account id used for the job. See the newgrp command in the Account Administration section of the Bridges User Guide to see how to change the Unix group currently in effect.

Bridges partitions

Each SLURM partition manages a subset of Bridges' resources. Each partition allocates resources to interactive sessions, batch jobs, and OnDemand sessions that request resources from it.

Know which partitions are open to you: Your Bridges allocations determine which partitions you can submit jobs to.

A "Bridges regular memory" allocation allows you to use Bridges' RSM (128GB) nodes. Partitions available to "Bridges regular memory" allocations are

RM, for jobs that will run on Bridges' RSM (128GB) nodes, and use one or more full nodes

RM-shared, for jobs that will run on Bridges' RSM (128GB) nodes, but share a node with other jobs

RM-small, for short jobs needing 2 full nodes or less, that will run on Bridges RSM (128GB) nodes

A "Bridges large memory" allocation allows you to use Bridges LSM and ESM (3TB and 12TB) nodes. There is one partition available to "Bridges large memory" allocations:

LM, for jobs that will run on Bridges' LSM and ESM (3TB and 12TB) nodes

A "Bridges GPU" allocation allows you to use Bridges' GPU nodes. Partitions available to "Bridges GPU" allocations are:

GPU, for jobs that will run on Bridges' GPU nodes, and use one or more full nodes

GPU-shared, for jobs that will run on Bridges' GPU nodes, but share a node with other jobs

GPU-small, for jobs that will use only one of Bridges' GPU nodes and 8 hours or less of wall time.

A "Bridges-AI" allocation allows you to you Bridges' Volta GPU nodes. There is one partition available to "Bridges-AI" allocations:

GPU-AI, for jobs that will run on Bridges' Volta 16 nodes or the DGX-2.

All the partitions use FIFO scheduling. If the top job in the partition will not fit, SLURM will try to schedule the next job in the partition. The scheduler follows policies to ensure that one user does not dominate the machine. There are also limits to the number of nodes and cores a user can simultaneously use. Scheduling policies are always under review to ensure best turnaround for users.

Partitions for "Bridges regular memory" allocations

There a three partitions available for "Bridges regular memory allocations": RM, RM-shared and RM-small.

Use your allocation wisely: To make the most of your allocation, use the shared partitions whenever possible. Jobs in the RM partition use of all the cores on a node, and incur Service Units (SU) for all 28 cores. Jobs in the RM-shared partition share nodes, and SUs accrue only for the number of cores they are allocated. The RM partition is the default for the sbatch command, while RM-small is the default for the interact command. See the discussion of the interact and sbatch commands in this document for more information.

Use the appropriate account id for your jobs: If you have more than one Bridges grant, be sure to use the correct SLURM account id for each job. See "Managing multiple grants".

For information on requesting resources and submitting jobs see the discussion of the interact or sbatch commands.

RM partition

Jobs in the RM partition run on Bridges' RSM (128GB) nodes. Jobs do not share nodes, and are allocated all 28 of the cores on each of the nodes assigned to them. A job in the RM partition incurs SUs for all 28 cores per node on its assigned nodes.

RM jobs can use more than one node. However, the memory space of all the nodes is not integrated. The cores within a node access a shared memory space, but cores in different nodes do not.

The internode communication performance for jobs in the RM partition is best when using 42 or fewer nodes.

When submitting a job to the RM partition, you should specify:

the number of nodes

the walltime limit

Sample interact command for the RM partition

An example of an interact command for the RM partition, requesting the use of 2 nodes for 30 minutes is

interact -p RM -N 2 -t 30:00

where:

-p indicates the intended partition

-N is the number of nodes requested

-t is the walltime requested in the format HH:MM:SS

Sample sbatch command for RM partition

An example of a sbatch command to submit a job to the RM partition, requesting one node for 5 hours is

sbatch -p RM -t 5:00:00 -N 1 myscript.job

where:

-p indicates the intended partition

-t is the walltime requested in the format HH:MM:SS

-N is the number of nodes requested

myscript.job is the name of your batch script

RM-shared partition

Jobs in the RM-shared partition run on (part of) a Bridges' RSM (128GB) node. Jobs will share a node with other jobs, but will not share cores. A job in the RM-shared partition will accrue SUs only for the cores allocated to it, so it will use fewer SUs than a RM job. It could also start running sooner.

RM-shared jobs are assigned memory in proportion to the number of requested cores. They get the fraction of the node's total memory in proportion to the number of cores requested. If the job exceeds this amount of memory it will be killed.

RM-small partition

Jobs in the RM-small partition run on Bridges' RSM (128GB) nodes, but are limited to at most 2 full nodes and 8 hours. Jobs can share nodes. Note that the memory space of all the nodes is not integrated. The cores within a node access a shared memory space, but cores in different nodes do not. When submitting a job to the RM-small partition, you should specify:

the number of nodes

the number of cores

the walltime limit

Sample interact command for the RM-small partition

Run in the RM-small partition using one node, 8 cores and 45 minutes of walltime.

interact -p RM-small -N 1 --ntasks-per-node=8 -t 45:00

where:

-p indicates the intended partition

-N requests one node

--ntasks-per-node requests the use of 8 cores

-t is the walltime requested in the format HH:MM:SS

Sample sbatch command for the RM-small partition

Submit a job to RM-small asking for 2 nodes and 6 hours of walltime.

sbatch -p RM-small -N 2 -t 6:00:00 myscript.job

where:

-p indicates the intended partition

-N requests the use of 2 nodes

-t is the walltime requested in the format HH:MM:SS

myscript.job is the name of your batch script

Summary of partitions for Bridges regular memory nodes

Partition name

RM

RM-shared

RM-small

Node type

128GB28 cores8TB on-node storage

128GB28 cores8TB on-node storage

128GB28 cores8TB on-node storage

Nodes shared?

No

Yes

Yes

Node default

1

1

1

Node max

168If you need more than 168, contact bridges@psc.edu to make special arrangements.

See also

Partitions for "Bridges large memory" allocations

There is one partition available for "Bridges large memory" allocations: LM.

Use the appropriate account id for your jobs: If you have more than one Bridges grant, be sure to use the correct SLURM account id for each job. See "Managing multiple grants".

For information on requesting resources and submitting jobs see the interact or sbatch commands.

LM partition

Jobs in the LM partition share nodes. Your memory space for an LM job is an integrated, shared memory space.

When submitting a job to the LM partition, you must

use the --mem option to request the amount of memory you need, in GB. Any value up to 12000GB can be requested There is no default memory value. Each core on the 3TB and 12TB nodes is associated with a fixed amount of memory, so the amount of memory you request determines the number of cores assigned to your job.

specify the walltime limit

You cannot:

specifically request a number of cores

SLURM will place jobs on either a 3TB or a 12TB node based on the memory request. Jobs asking for 3000GB or less will run on a 3TB node. If no 3TB nodes are available but a 12TB node is available, those jobs will run on a 12TB node.

Once your job is running, the environment variable SLURM_NTASKS tells you the number of cores assigned to your job.

Sample interact command for LM

Run in the LM partition and request 2TB of memory. Use the wall time default of 30 minutes.

interact -p LM --mem=2000GB

where:

-p indicates the intended partition (LM)

--mem is the amount of memory requested

Sample sbatch command for the LM partition

A sample sbatch command for the LM partition requesting 10 hours of wall time and 6TB of memory is:

sbatch -p LM - t 10:00:00 --mem 6000GB myscript.job

where:

-p indicates the intended partition (LM)

-t is the walltime requested in the format HH:MM:SS

--mem is the amount of memory requested

myscript.job is the name of your batch script

Summary of partition for Bridges large memory nodes

Partition name

LM

LSM nodes

ESM nodes

Node type

3TB RAM16TB on-node storage

12TB RAM64TB on-node storage

Nodes shared?

Yes

Yes

Node default

1

1

Node max

8

4

Cores

Jobs are allocated 1 core/48GB of memory requested.

Jobs are allocated 1 core/48GB of memory requested.

Walltime default

30 mins

30 mins

Walltime max

14 days

14 days

Memory

Up to 3000GB

Up to 12,000GB

See also

Partitions for "Bridges GPU" allocations

There are three partitions available for "Bridges GPU" allocations: GPU, GPU-shared and GPU-small.

Use your allocation wisely: To make the most of your allocation, use the shared partitions whenever possible. Jobs in the GPU partition use all of the cores on a node, and accrue SU costs for every coew. Jobs in the GPU-shared partition share nodes, and only incur SU cost for the number of cores they are allocated.

Use the appropriate account id for your jobs: If you have more than one Bridges grant, be sure to use the correct SLURM account id for each job. See "Managing multiple grants".

For information on requesting resources and submitting jobs see the interact or sbatch commands.

GPU partition

Jobs in the GPU partition use Bridges' GPU nodes. Note that Bridges has 2 types of GPU nodes: K80s and P100s. See the System Configuration section of this User Guide for the details of each type.

Jobs in the GPU partition do not share nodes, so jobs are allocated all the cores and all of the GPUs associated with the nodes assigned to them . Your job will incur SU costs for all of the cores on your assigned nodes.

The memory space across nodes is not integrated. The cores within a node access a shared memory space, but cores in different nodes do not.

When submitting a job to the GPU partition, you must use the --gres=type option to specify

the type of node you want, K80 or P100. K80 is the default if no type is specified.

Notes:The value of the --gres-gpu option indicates the type and number of GPUs you want.For groupname, username and path-to-directory you must substitute your Unix group, username and appropriate directory path.

GPU-shared partition

Jobs in the GPU-shared partition run on Bridges's GPU nodes. Note that Bridges has 2 types of GPU nodes: K80s and P100s. See the System Configuration section of this User Guide for the details of each type.

Jobs in the GPU-shared partition share nodes, but not cores. By sharing nodes your job will use fewer Service Units. It could also start running sooner.

You will always run on (part of) one node in the GPU-shared partition.Your jobs will be allocated memory in proportion to the number of requested GPUs. You get the fraction of the node's total memory in proportion to the fraction of GPUs you requested. If your job exceeds this amount of memory it will be killed.

When submitting a job to the GPU-shared partition, you must specify the number of GPUs. You should also specify:

the type of GPU node you want, K80or P100, with the --gres=type option to the interact or sbatch commands. K80 is the default if no type is specified. See the sbatch command options for more details.

the walltime limit

Sample interact command for GPU-shared

Run in the GPU-shared partition and ask for 4 K80 GPUs and 8 hours of wall time.

Notes:The option --gres-gpu indicates the number and type of GPUs you want.For groupname, username and path-to-directory you must substitute your Unix group, username, and appropriate directory path.

GPU-small

Jobs in the GPU-small partition run on one of Bridges' P100 GPU nodes. Your jobs will be allocated memory in proportion to the number of requested GPUs. You get the fraction of the node's total memory in proportion to the fraction of GPUs you requested. If your job exceeds this amount of memory it will be killed.

When submitting a job to the GPU-small partition, you must specify the number of GPUs with the --gres=gpu:p100:n option to the interact or sbatch command. In this partition, n can be 1 or 2. You should also specify the walltime limit.

Sample interact command for GPU-small

Run in the GPU-small partition and ask for 2 P100 GPUs and 2 hours of wall time.

interact -p GPU-small --gres=gpu:p100:2 -t 2:00:00

where:

-p indicates the intended partition

--gres=gpu:P100:2 requests the use of 2 P100 GPUs

-t is the walltime requested in the format HH:MM:SS

Sample sbatch command for GPU-small

Submit a job to the GPU-small partition using 2 P100 GPUs and 1 hour of wall time.

sbatch -p GPU-small --gres=gpu:p100:2 -t 1:00:00 myscript.job

where:

-p indicates the intended partition

--gres=gpu:P100:2 requests the use of 2 P100 GPUs

-t is the walltime requested in the format HH:MM:SS

myscript.job is the name of your batch script

Summary of partitions for Bridges' GPU nodes

Partition name

GPU

GPU-shared

GPU-small

P100 nodes

K80 nodes

P100 nodes

K80 nodes

P100 nodes

Node type

2 GPUs2 16-core CPUs8TB on-node storage

4 GPUs2 14-core CPUS8TB on-node storage

2 GPUs2 16-core CPUs8TB on-node storage

4 GPUs2 14-core CPUs8TB on-node storage

2 GPUs2 16-core CPUs8TB on-node storage

Nodes shared?

No

No

Yes

Yes

No

Node default

1

1

1

1

1

Node max

4Limit of 8 GPUs/job.Because there are 2 GPUs on each P100 node, you can request at most 4 nodes.

2Limit of 8 GPUs/job.Because there are 4 GPUs on each K80 node, you can request at most 2 nodes.

We strongly recommend the use of Singularity containers on the AI nodes, especially on the DGX-2. We have installed containers for many popular AI packages on Bridges for you to use, but you can create your own if you like.

For more information on Singularity and the containers available on Bridges:

Use the appropriate account id for your jobs: If you have more than one Bridges grant, be sure to use the correct SLURM account id for each job. See "Managing multiple grants".

For information on requesting resources and submitting jobs see the interact or sbatch commands.

Using module files on Bridges-AI

The Module package provides for the dynamic modification of a users's environment via module files. Module files manage necessary changes to the environment, such as adding to the default path or defining environment variables, so that you do not have to manage those definitions and paths manually. Before you can use module files in a batch job on Bridges-AI, you must issue the following command:

Monitoring memory usage

It can be useful to find the memory usage of your jobs. For example, you may want to find out if memory usage was a reason a job failed.

You can determine a job's memory usage whether it is still running or has finished. To determine if your job is still running, use the squeue command.

squeue -j nnnnnn -O state

where nnnnnn is the jobid.

For running jobs: srun and top or sstat

You can use the srun and top commands to determine the amount of memory being used.

srun --jobid=nnnnnn top -b -n 1 | grep userid

For nnnnnn substitute the jobid of your job. For 'userid' substitute your userid. The RES field in the output from top shows the actual amount of memory used by a process. The top man page can be used to identify the fields in the output of the top command.

Sample batch scripts for Bridges

Both sample batch scripts for some popular software packages and sample batch scripts for general use on Bridges are available.

For more information on how to run a job on Bridges, what partitions are available, and how to submit a job, see the Running Jobs section of the Bridges User Guide.

Sample batch scripts for popular software packages

Sample scripts for some popular software packages are available on Bridges in the directory /opt/packages/examples. There is a subdirectory for each package, which includes the script along with input data that is required and typical output.

See the documentation for a particular package for more information on using it and how to test any sample scripts that may be available.

Sample batch scripts for common types of jobs

Sample Bridges batch scripts for common job types are given in this document.

Note that in each sample script:

The bash shell is used, indicated by the first line '!#/bin/bash'. If you use a different shell some Unix commands will be different.

For username and groupname you must substitute your username and your appropriate Unix group.

The variable $SLURM_NTASKS gives the total number of cores requested in a job. In this example $SLURM_NTASKS will be 56 because the -N option requested 2 nodes and the --ntasks-per-node option requested all 28 cores on each node.

The export command sets I_MPI_JOB_RESPECT_PROCESS_PLACEMENT so that your task placement settings are effective. Otherwise, the SLURM defaults are in effect.

The srun commands are used to copy files between pylon5 and the $LOCAL file systems on each of your nodes.

The first srun command assumes you have two files named input.0 and input.1 in your pylon5 file space. It will copy input.0 and input.1 to, respectively, the $LOCAL file systems on the first and second nodes allocated to your job.

The second srun command will copy files named output.* back from your $LOCAL file systems to your pylon5 file space before your job ends. In this command '*' functions as the usual Unix wildcard.

Sample script for job array

This script will generate five jobs that will each run on a separate core on the same node. The value of the variable SLURM_ARRAY_TASK_ID is the core number, which, in this example, will range from 1 to 5. Good candidates for job array jobs are jobs that can use only this core index to determine the different processing path for each job. For more information about job array jobs see the sbatch man page and the online SLURM documentation.

Paths for Bridges file spaces

For all file transfer methods other than cp, you must always use the full path for your Bridges files. The start of the full paths for your Bridges directories are:

Home directory /home/username

Pylon5 directory /pylon5/Unix-group/username

The command id -Gn will show all of your valid Unix-groups. You have a pylon5 directory for each grant you have.

Transfers into your Bridges home directory

Your home directory quota is 10GB, so large files cannot be stored there; they should be copied into one of your pylon file spaces instead. Exceeding your home directory quota will prevent you from writing more data into your home directory and will adversely impact other operations you might want to perform.

rsync

You can use the rsync command to copy files to and from Bridges. A sample rsync command to copy to a Bridges directory is

Substitute your userid for 'joeuser'. Make sure you use the correct group name in your target directory. By default, rsync will not copy older files with the same name in place of newer files in the target directory. It will overwrite older files.

We recommend the rsync options -rltDvp. See the rsync man page for information on these options and other you options you might want to use. We also recommend the option

-oMACS=umac-64@openssh.com

If you use this option your transfer will use a faster data validation algorithm.

You may to want to put your rsync command in a loop to insure that it completes. A sample loop is

This loop will try your rsync command 20 times. If it succeeds it will exit. If an rsync invocation is unsuccessful the system will try again and pick up where it left off. It will copy only those files that have not already been transferred. You can put this loop, with your rsync command, into a batch script and run it with sbatch.

scp

To use scp for a file transfer you must specify a source and destination for your transfer. The format for either source or destination is

username@machine-name:path/filename

For transfers involving Bridges, username is your PSC username. The machine-name should be given as data.bridges.psc.edu. This is the name for a high-speed data connector at PSC. We recommend using it for all file transfers using scp involving Bridges. Using it prevents file transfers from disrupting interactive use on Bridges' login nodes.

sftp

To use sftp, first connect to the remote machine:

sftp username@machine-name

When Bridges is the remote machine, use your PSC userid as username. The Bridges machine-name should be specified as data.bridges.psc.edu. This is the name for a high-speed data connector at PSC. We recommend using it for all file transfers using sftp involving Bridges. Using it prevents file transfers from disrupting interactive use on Bridges' login nodes.

You will be prompted for your password on the remote machine. If Bridges is the remote machine enter your PSC password.

You can then enter sftp subcommands, like put to copy a file from the local system to the remote system, or get to copy a file from the remote system to the local system.

To copy files into Bridges you must either cd to the proper directory or use full pathnames in your file transfer commands. See Paths for Bridges file spaces for details.

Two-factor Authentication

If you are required to use two-factor authentication (TFA) to access Bridges' filesystems, you must enroll in XSEDE DUO. Once that is complete, use scp or sftp to transfer files to/from Bridges.

TFA users must use port 2222 and XSEDE Portal usernames and passwords. The machine name for these transfers is data.bridges.psc.edu.

In the examples below, myfile is the local filename, XSEDE-username is your XSEDE Portal username and /path/to/file is the full path to the file on a Bridges filesystem. Note that -P ( capital P) is necessary.

scp

Transfer a file from a local machine to Bridges:

scp -P 2222 myfileXSEDE-username@data.bridges.psc.edu:/path/to/file

Transfer a file from Bridges to a local machine:

scp -P 2222 XSEDE-username@data.bridges.psc.edu:/path/to/filemyfile

sftp

Use sftp interactively:

sftp -P 2222 XSEDE-username@data.bridges.psc.edu

Then use the put command to copy a file from the local machine to Bridges, or the get command to transfer a file from Bridges to the local machine.

Graphical SSH client

If you are using a graphic SSH client, configure it to connect to data.bridges.psc.edu on port 2222/TCP. Login using your XSEDE Portal username and password.

Globus

Globus can be used for any file transfer to Bridges. It tracks the progress of the transfer and retries when there is a failure; this makes it especially useful for transfers involving large files or many files.

To use Globus to transfer files you must authenticate either via a Globus account or with InCommon credentials.

Click the 'Log On' button. You will be taken to the web login page for your institution.

Login with your username and password for your institution.

If your institution has an additional login requirement (e.g., Duo), authenticate to that as well.

After successfully authenticating to your institution's web login interface, you will be returned to the CILogon webpage. Note the boxed section near the top that lists a field named 'Certificate Subject'.

Send your Certificate Subject string to PSC

In the CILogon webpage, select and copy the Certificate Subject text. Take care to get the entire text string if it is broken up onto multiple lines.

Send email to support@psc.edu. Paste your Certificate Subject field into the message, asking that it be mapped to your PSC username.

Your CILogin Certificate Subject information will be added within one business day, and you will be able to begin transferring files to and from Bridges.

Globus endpoints

Once you have the proper authentication you can initiate file transfers from the Globus site. A Globus transfer requires a Globus endpoint, a file path and a file name for both the source and destination. The endpoints for Bridges are:

psc#bridges-xsede if you are using an XSEDE User Portal account for authentication

psc#bridges-cilogon if you are using InCommon for authentication

These endpoints are owned by psc@globusid.org. If you use DUO MFA for your XSEDE authentication, you do not need to because you cannot use it with Globus. You must always specify a full path for the Bridges file systems. See Paths for Bridges file spaces for details.

Globus-url-copy

The globus-url-copy command can be used if you have access to Globus client software. Both the globus-url-copy and myproxy-logon commands are available on Bridges, and can be used for file transfers internal to the PSC.

To use globus-url-copy you must have a current user proxy certificate. The command grid-proxy-info will tell you if you have current user proxy certificate and if so, what the remaining life of your certificate is.

Use the myproxy-logon command to get a valid user proxy certificate if any one of these applies:

you get an error from the grid-proxy-info command

you do not have a current user proxy certificate

the remaining life of your certificate is not sufficient for your planned file transfer

Transfer rates

PSC maintains a Web page at http://speedpage.psc.edu that lists average data transfer rates between all XSEDE resources. If your data transfer rates are lower than these average rates or you believe that your file transfer performance is subpar, send email to bridges@psc.edu. We will examine approaches for improving your file transfer performance.

Virtual Machines

A Virtual Machine (VM) is a portion of a physical machine that is partitioned off through software so that it acts as an independent physical machine.

You should indicate that you want a VM when you apply for time on Bridges.

When you have an active Bridges' grant, use the VM Request form to request a VM. This form requests information about the software and hardware resources you need for your VM and your reason for requesting a VM. Your request will be evaluated by PSC staff for its suitability.You will be contacted in one business day about your request.

Why use a VM?

If you need a persistent environment you need to use a virtual machine (VM). Examples of a need for a persistent environment are a Web server with a database backend or just a persistent database.

If you can use a Singularity container rather than a VM, you should use the container. You can set up your Singularity container yourself without any intervention by PSC staff. Also, a VM accrues Service Units (SU) the entire time it is set up, whether or not it is being actively used. Because they only exist while you are using them, containers only accrue SUs for the time during which they are executing.

A VM provides you with control over your environment, but you will have access to the computing power, memory capacity and file spaces of Bridges.

Common uses of VMs include hosting database and web servers. These servers can be restricted just to you or you can open them up to outside user communities to share your work. You can also connect your database and web servers and other processing components in a complex workflow.

VMs provide several other benefits. Since the computing power behind the VM is a supercomputer, sufficient resources are available to support multiple users. Since each VM acts like an independent machine, user security is heightened. No outside users can violate the security of your independent VM. However, you can allow other users to access your VM if you choose.

A VM can be customized to meet your requirements. PSC will set up the VM and give you access to your database and web server at a level that matches your requirements.

To discuss whether a VM would be appropriate for your research project send email to bridges@psc.edu.

Downtime

VMs are affected by system downtime, and will not be available during an outage. Scheduled downtimes are announced in advance.

Data backups

It is your responsiblity to backup any important data to another location outside of the VM. PSC will make infrequent snapshots of VMs for recovery from system failure, but cannot be responsible for managing your data.

Grant expiration

When your grant expires, your VM will be suspended. You have a 3-month grace period to request via email to bridges@psc.edu that it be reactivated so that you can move data from the VM. Three months after your grant expires, the VM will be removed. Please notify bridges@psc.edu if you need help moving your data during the grace period.

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